The problem of explainable AI is very broad and the term explainable is still debated in philosophical texts [1]} (“What is an Explanation?”). A comprehensive survey of explainable AI has been published by Arrieta et al. [2]} and extensive teaching materials have been made available by Lakkaraju et al. [3]}.
Our approach can be related to “Explanation of Deep Network Representation” (roles of layers, individual units, and representation vectors) according to the Deep Learning-specific taxonomy presented by Arrieta et al. in [2]}, Fig. 11.
Our utilization-based approach is inspired by exploring relationships between biological neural circuits and AI model computation graphs as discussed by Olah et al. in [5]}. Next, the related work is presented with respect to the three formulated problems.
